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описание
How many years of relevant commercial experience do you have for this role?* • What is your current city of residence?* • What is your official notice period with your current employer, as per your offer letter?*
The client is building a healthcare platform focused on scaling its core product capabilities through AI and cloud-native technologies. The initiative combines product engineering, machine learning, and platform architecture, with a dedicated engineering team responsible for platform architecture and accelerating product development.
задачи
Lead the ideation, design, and execution of AI proofs of concept and end-to-end machine learning systems, from research and experimentation through scalable production deployment;
Develop and implement machine learning and AI systems across Computer Vision, NLP, and Generative AI to solve business and product challenges;
Collaborate with engineering, product management, and business stakeholders to translate business objectives into scalable AI capabilities and production-ready solutions;
Architect and maintain production-grade AI systems throughout their lifecycle, including data ingestion, feature engineering, model training, retrieval pipelines, orchestration, deployment, evaluation, and monitoring;
Design and implement RAG pipelines, semantic search systems, and multi-agent workflows, selecting architectures based on performance, latency, cost, and compliance constraints;
Design evaluation frameworks for machine learning and generative AI systems, including offline evaluation, human-in-the-loop validation, automated scoring, and production monitoring;
Optimize AI systems for scalability, latency, and cost efficiency, including batching, caching, quantization, and model selection;
Design and apply mathematical models and statistical methods to analyze large multimodal datasets, including time series, classification, regression, and representation learning;
Collaborate on AI architecture decisions and contribute to technical roadmap planning, identifying opportunities for AI adoption across the organization;
Mentor and provide technical guidance to junior ML engineers and data scientists, promoting engineering best practices and continuous learning;
Stay current with AI and machine learning research, tools, and architectures, evaluating and introducing relevant advancements.
требования
5+ Years of hands-on experience in machine learning, AI research, or data science, with a track record of delivering production-grade ML solutions;
Master’s or Ph.D. in Computer Science, Data Science, Machine Learning, or a related quantitative field;
Strong expertise in supervised and unsupervised learning, time series modeling, classification, regression, and model evaluation;
Hands-on experience developing production systems in Computer Vision, NLP, and Generative AI;
Experience building LLM-based applications, including RAG pipelines, agentic workflows, semantic search, and tool-using AI systems;
Experience designing generative AI evaluation frameworks, including prompt evaluation, hallucination detection, and performance monitoring;
Proficiency in Python and the ML ecosystem, including PyTorch, TensorFlow, Hugging Face, NumPy, Pandas, and scikit-learn;
Experience with LLM orchestration and serving frameworks such as LangChain, LlamaIndex, DSPy, Haystack, or equivalent;
Experience deploying and scaling AI systems on AWS, Azure, or GCP, including managed ML services and containerized deployments;
Experience building and maintaining end-to-end ML pipelines for data processing, model training, deployment, and monitoring;
Understanding of responsible AI principles, including bias mitigation, safety guardrails, and governance;
Proficiency with Git and software engineering best practices for collaborative ML development;
Excellent communication skills and ability to explain complex technical concepts to technical and non-technical stakeholders;
Nice to have: MLOps tooling and workflows, including MLflow, Apache Airflow, Terraform, and CI/CD pipelines for model deployment; model serving and inference optimization tools such as vLLM; feature stores, real-time data pipelines, or streaming AI systems; distributed or large-scale model training frameworks; multimodal AI systems; fine-tuning LLMs or parameter-efficient training techniques such as LoRA and PEFT; contributions to open-source ML projects or peer-reviewed publications.
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